| 001. Short introduction to computer vision.mp4 | 15.3 MB | ||
| 001. Short introduction to computer vision.srt | 7.1 KB | ||
| 002. Digital images.mp4 | 12.2 MB | ||
| 002. Digital images.srt | 5.1 KB | ||
| 003. Structure of human eye and vision.mp4 | 22.3 MB | ||
| 003. Structure of human eye and vision.srt | 8.4 KB | ||
| 004. Color models.mp4 | 57.7 MB | ||
| 004. Color models.srt | 21.5 KB | ||
| 005. Image processing goals and tasks.mp4 | 10.8 MB | ||
| 005. Image processing goals and tasks.srt | 3.6 KB | ||
| 006. Contrast and brightness correction.mp4 | 19.7 MB | ||
| 006. Contrast and brightness correction.srt | 7 KB | ||
| 007. Image convolution.mp4 | 26 MB | ||
| 007. Image convolution.srt | 9.6 KB | ||
| 008. Edge detection.mp4 | 31.9 MB | ||
| 008. Edge detection.srt | 11.7 KB | ||
| 009. Recap Image classification.mp4 | 32.4 MB | ||
| 009. Recap Image classification.srt | 10.8 KB | ||
| 010. AlexNet, VGG and Inception architectures.mp4 | 43.8 MB | ||
| 010. AlexNet, VGG and Inception architectures.srt | 14.2 KB | ||
| 011. ResNet and beyond.mp4 | 43.2 MB | ||
| 011. ResNet and beyond.srt | 12.3 KB | ||
| 012. Fine-grained image recognition.mp4 | 25.4 MB | ||
| 012. Fine-grained image recognition.srt | 7.5 KB | ||
| 013. Detection and classification of facial attributes.mp4 | 24.1 MB | ||
| 013. Detection and classification of facial attributes.srt | 8.1 KB | ||
| 014. Content-based image retrieval.mp4 | 31.6 MB | ||
| 014. Content-based image retrieval.srt | 9.3 KB | ||
| 015. Computing semantic image embeddings using convolutional neural networks.mp4 | 35.6 MB | ||
| 015. Computing semantic image embeddings using convolutional neural networks.srt | 11 KB | ||
| 016. Employing indexing structures for efficient retrieval of semantic neighbors.mp4 | 37.3 MB | ||
| 016. Employing indexing structures for efficient retrieval of semantic neighbors.srt | 11.7 KB | ||
| 017. Face verification.mp4 | 25.2 MB | ||
| 017. Face verification.srt | 7.9 KB | ||
| 018. The re-identification problem in computer vision.mp4 | 21.1 MB | ||
| 018. The re-identification problem in computer vision.srt | 6.8 KB | ||
| 019. Facial keypoints regression.mp4 | 25.6 MB | ||
| 019. Facial keypoints regression.srt | 7.5 KB | ||
| 020. CNN for keypoints regression.mp4 | 23.2 MB | ||
| 020. CNN for keypoints regression.srt | 7.2 KB | ||
| 021. Object detection problem.mp4 | 22.4 MB | ||
| 021. Object detection problem.srt | 9.8 KB | ||
| 022. Sliding windows.mp4 | 11.8 MB | ||
| 022. Sliding windows.srt | 4.7 KB | ||
| 023. HOG-based detector.mp4 | 9.1 MB | ||
| 023. HOG-based detector.srt | 3.4 KB | ||
| 024. Detector training.mp4 | 11.7 MB | ||
| 024. Detector training.srt | 4.4 KB | ||
| 025. Viola-Jones face detector.mp4 | 19.4 MB | ||
| 025. Viola-Jones face detector.srt | 8 KB | ||
| 026. Attentional cascades and neural networks.mp4 | 12.2 MB | ||
| 026. Attentional cascades and neural networks.srt | 4.8 KB | ||
| 027. Region-based convolutional neural network.mp4 | 10.7 MB | ||
| 027. Region-based convolutional neural network.srt | 4.3 KB | ||
| 028. From R-CNN to Fast R-CNN.mp4 | 17.8 MB | ||
| 028. From R-CNN to Fast R-CNN.srt | 6.9 KB | ||
| 029. Faster R-CNN.mp4 | 15.8 MB | ||
| 029. Faster R-CNN.srt | 5.7 KB | ||
| 030. Region-based fully-convolutional network.mp4 | 8.5 MB | ||
| 030. Region-based fully-convolutional network.srt | 3.1 KB | ||
| 031. Single shot detectors.mp4 | 14.5 MB | ||
| 031. Single shot detectors.srt | 2.5 KB | ||
| 032. Speed vs. accuracy tradeoff.mp4 | 7.1 MB | ||
| 032. Speed vs. accuracy tradeoff.srt | 2.5 KB | ||
| 033. Fun with pedestrian detectors.mp4 | 5.8 MB | ||
| 033. Fun with pedestrian detectors.srt | 1.6 KB | ||
| 034. Introduction to video analysis.mp4 | 12.7 MB | ||
| 034. Introduction to video analysis.srt | 5.2 KB | ||
| 035. Optical flow.mp4 | 17.3 MB | ||
| 035. Optical flow.srt | 7.8 KB | ||
| 036. Deep learning in optical flow estimation.mp4 | 19 MB | ||
| 036. Deep learning in optical flow estimation.srt | 8.6 KB | ||
| 037. Visual object tracking.mp4 | 18.7 MB | ||
| 037. Visual object tracking.srt | 8.1 KB | ||
| 038. Examples of visual object tracking methods.mp4 | 42.9 MB | ||
| 038. Examples of visual object tracking methods.srt | 20.8 KB | ||
| 039. Multiple object tracking.mp4 | 18.2 MB | ||
| 039. Multiple object tracking.srt | 8.1 KB | ||
| 040. Examples of multiple object tracking methods.mp4 | 26.3 MB | ||
| 040. Examples of multiple object tracking methods.srt | 12 KB | ||
| 041. Introduction to action recognition.mp4 | 21.9 MB | ||
| 041. Introduction to action recognition.srt | 9.3 KB | ||
| 042. Action classification.mp4 | 26.6 MB | ||
| 042. Action classification.srt | 11.9 KB | ||
| 043. Action classification with convolutional neural networks.mp4 | 18.8 MB | ||
| 043. Action classification with convolutional neural networks.srt | 8.2 KB | ||
| 044. Action localization.mp4 | 22.4 MB | ||
| 044. Action localization.srt | 10.1 KB | ||
| 045. Image segmentation.mp4 | 16 MB | ||
| 045. Image segmentation.srt | 4.8 KB | ||
| 046. Oversegmentation.mp4 | 17.8 MB | ||
| 046. Oversegmentation.srt | 5.4 KB | ||
| 047. Deep learning models for image segmentation.mp4 | 32.7 MB | ||
| 047. Deep learning models for image segmentation.srt | 10.1 KB | ||
| 048. Human pose estimation as image segmentation.mp4 | 33.4 MB | ||
| 048. Human pose estimation as image segmentation.srt | 10.9 KB | ||
| 049. Style transfer.mp4 | 22.7 MB | ||
| 049. Style transfer.srt | 6.6 KB | ||
| 050. Generative adversarial networks.mp4 | 29.5 MB | ||
| 050. Generative adversarial networks.srt | 9.9 KB | ||
| 051. Image transformation with neural networks.mp4 | 22.7 MB | ||
| 051. Image transformation with neural networks.srt | 5.6 KB | ||
| [FTU Forum].url | 204.8 B | ||
| [FreeCoursesOnline.Me].url | 102.4 B | ||
| [FreeTutorials.Us].url | 102.4 B | ||
| ▲ 105 total files | |||
[COURSERA] DEEP LEARNING IN COMPUTER VISION [FCO]
About this course: Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.
For more Coursera and other Courses >>> https://www.freecoursesonline.me/
For More Udemy Free Courses >>> http://www.freetutorials.us
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 4 GB | freecoursewb | 4 months | 12 | 10 | |
| 2.66 GB | Prom3th3uS | 1 year | 7 | 1 | |
| 5.31 GB | Prom3th3uS | 2 years | 14 | 1 | |
| 1.5 GB | tutsnode | 5 years | 4 | 1 | |
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[ FreeCourseWeb ] Coursera - TensorFlow 2 for Deep Learning Specialization Posted by
freecoursewb in Other
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1.4 GB | freecoursewb | 5 years | 0 | 3 |
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